Evaluation of image and video processing takes place in three ways: computational complexity, storage requirements of the processed data, and visual quality of the reconstituted image or video data. Image and video processing generally utilizes some form of sub-sampling. Sub-sampling is the process of selecting, for example, certain pixels from an image which are good predictors of the pixels surrounding the sample in the original. Storage or transmission occurs only for the selected sub-samples, rather than the entire image, thereby reducing the size. Quality of the reconstituted image depends upon the effectiveness of the sub-samples as predictors of the pixels in the image. Irregular sub-sampling selects samples in the image so to improve the prediction power, which will generally lead to an irregular sampling pattern (e.g., more samples positioned near object edges and high texture regions, and less samples in uniform low texture background regions) rather than a regular sampling pattern.
Improving predictive strength requires finding better sub-samples or using more sub-samples. Using more sub-samples reduces the storage savings, while finding better sub-samples requires greater computational complexity. Prior art techniques used for image and video processing are primarily block based techniques with simple filters. However, these techniques do not fully exploit the coupled nature of pixel based selection of sample position, sample level variation, and adaptive filtering.